ANALYSIS OF MODIS - MISR CROSS-CALIBRATION

1. ANALYSIS OF MODIS - MISR CROSS-CALIBRATION A. Lyapustin, Y. Wang (UMBC), J. Xiong, R. Wolfe (GSFC), R. Kahn, C. Bruegge (JPL), K. Thome (UA), A. Ignatov (NOAA), S. Platnick (GSFC)

2. AERONET-based Surface Reflectance Validation Network (ASRVN) A. Lyapustin, Y. Wang (GEST UMBC/NASA GSFC), B. Holben, J. Privette (GSFC) • Goal – development of globally representative dataset of surface reflectance • Surface BRF and albedo are important EOS products, not yet validated globally. • Knowledge of SR is required by CM, cloud properties, and aerosol algorithms. • THEORETICAL BASIS • 3D Radiative Transfer(Lyapustin & Knyazikhin, Appl. Opt., 2001; 2002) • variable anisotropic surface; • arbitrary spatial resolution; • semi-analytical, accurate and fast due to parameterizations. • Accurate Modeling of Gaseous Absorption • Inversion with MRPVMISR and LSRTMODIS BRF Models • PRODUCTS • BRF, Albedo (spectral & SW broadband) • Surface Radiative Fluxes, PAR • Spectral Regression Coefficients (2.1 mBlue, Red) • APPLICATIONS • Validation of BRF & Albedo over Heterogeneous Surfaces • Calibration Analysis • Vicarious calibration • Cross-calibration of different sensors • Detection of calibration trend based on a time series of surface reflectance (climate quality). MISR MODIS ETM+ VIIRS

3. Cross-Calibration Analysis Comparison of MISR (top) and MODIS TERRA (bottom) ASRVN albedo over Mongu, Zambia

4. MODIS vs MISR Albedo – cont.(GSFC, USA) Artifacts of measurements Cloudy pixels

5. Regression of TOA Reflectance (3535 km2, >50% cloudy)

6. Band-pass functions of MISR (red) and MODIS land (blue) spectral channels Simulation Model: • SHARM_IPC_Mie code: • arbitrary band-pass function (MISR; MODIS; VIIRS; ETM+); • absorption of 7 major atmospheric gases (H2O, CO2, O3, CH4, NO2, CO, N2O): LBL absorption is modeled using HITRAN-2000 database and Voigt profile, continuum absorption model of AER (Clough et al.); • atmospheric profiles of Standard Models, solar irradiance model of Kurucz (MODTRAN3.0); • spectral resolution 0.01 – 1 cm-1; • full multiple scattering RT (code SHARM) with exactly calculated single scattering, Delta-M method for clouds; • Aerosol (Mie) model: • bi-modal log-normal size distribution (urban-industrial, biomass burning, dust/maritime models from Dubovik et al., 2002). • Surface models: • spectral albedo from ASTER and USGS spectral libraries; • Water cloud models: • log-normal size distribution, r=6, 10, 15, 20 m, =0.1 m; c=3.7 – 130.

7. rc 6 10 15 20 All AB 0.993 0.988 0.982 0.978 0.989 AR 1.038 1.044 1.045 1.046 1.041 Hc (1-5km, 0.4 cm RH) RH (0.4-2-5 cm, Hc=2 km) AB 0.980-0.987 - AG - - AR 1.036-1.039 1.042-1.046-1.061 ANir - 1.002-1.008-1.017 Simulated Regression of TOA Reflectance for Water Clouds Table 1. Dependence of slope on droplet size. Simulations were done with Hc=2km, RH=2cm, 1976 US Standard Atmospheric Profile. Table 2. Effect of cloud top height and column water vapor on the slope of regression.

8. Summary Table 3. Summary of regression coefficients Conclusions • We developed two independent methods to evaluate calibration bias between MODIS TERRA and MISR: • The first one derives bias as a difference between observed and theoretical regression coefficients for the TOA reflectance. • The second one evaluates bias based on statistical matching of the ASRVN albedo products from MODIS and from MISR over a large number of AERONET sites. The estimates from both methods agree well, except in the red band, where the albedo matching technique predicts about twice as high difference. • Conclusions for the first method: • Clouds prove to be a reliable stable target for the cross-calibration analysis. • Comparison of MODIS-MISR regression lines obtained from measurements and from simulations allows to evaluate the difference in the gain coefficient. • Our analysis suggests the following band gain difference: Blue – 5.8%, Green – 3.1%, Red – 1.2%.